Learning Discriminative Features for Image Retrieval

Abstract

Discriminative local features obtained from activations of convolutional neural networks have proven to be essential for image retrieval. To improve retrieval performance, many recent works aim to obtain more powerful and discriminative features. In this work, we propose a new attention layer to assess the importance of local features and assign higher weights to those more discriminative. Furthermore, we present a scale and mask module to filter out the meaningless local features and scale the major components. This module not only reduces the impact of the various scales of the major components in images by scaling them on the feature maps, but also filters out the redundant and confusing features with the MAX-Mask. Finally, the features are aggregated into the image representation. Experimental evaluations demonstrate that the proposed method outperforms the state-of-the-art methods on standard image retrieval datasets.